Protein Structure Function Research Articles

Prediction of mutation-induced protein stability changes based on the geometric representations learned by a self-supervised method

BackgroundThermostability is a fundamental property of proteins to maintain their biological functions. Predicting protein stability changes upon mutation is important for our understanding protein structure–function relationship, and is also of great interest in protein engineering and pharmaceutical design.ResultsHere we present mutDDG-SSM, a deep learning-based framework that uses the geometric representations encoded in protein structure to predict the mutation-induced protein stability changes. mutDDG-SSM consists of two parts: a graph attention network-based protein structural feature extractor that is trained with a self-supervised learning scheme using large-scale high-resolution protein structures, and an eXtreme Gradient Boosting model-based stability change predictor with an advantage of alleviating overfitting problem. The performance of mutDDG-SSM was tested on several widely-used independent datasets. Then, myoglobin and p53 were used as case studies to illustrate the effectiveness of the model in predicting protein stability changes upon mutations. Our results show that mutDDG-SSM achieved high performance in estimating the effects of mutations on protein stability. In addition, mutDDG-SSM exhibited good unbiasedness, where the prediction accuracy on the inverse mutations is as well as that on the direct mutations.ConclusionMeaningful features can be extracted from our pre-trained model to build downstream tasks and our model may serve as a valuable tool for protein engineering and drug design.

A modular inquiry-based semester theme that integrates data science education and bioinformatics in protein structure function courses.

With an exponential growth in biological data and computing power, familiarity with bioinformatics has become a demanding and popular skill set both in academia and industry. There is a need to increase students' competencies to be able to take on bioinformatic careers, to get them familiarized with scientific professions in data science and the academic training required to pursue them, in a field where demand outweighs the supply. Here we implemented a set of bioinformatic activities into a protein structure and function course of a graduate program. Concisely, students were given hands-on opportunities to explore the bioinformatics-based analyses of biomolecular data and structural biology via a semester-long case study structured as inquiry-based bioinformatics exercises. Towards the end of the term, the students also designed and presented an assignment project that allowed them to document the unknown protein that they identified using bioinformatic knowledge during the term. The post-module survey responses and students' performances in the lab module imply that it furthered an in-depth knowledge of bioinformatics. Despite having not much prior knowledge of bioinformatics prior to taking this module students indicated positive feedback. The students got familiar with cross-indexed databases that interlink important data about proteins, enzymes as well as genes. The essential skillsets honed by this research-based bioinformatic pedagogical approach will empower students to be able to leverage this knowledge for their future endeavours in the bioinformatics field.

The inorganic pyrophosphatases of microorganisms: a structural and functional review

Pyrophosphatases (PPases) are enzymes that catalyze the hydrolysis of pyrophosphate (PPi), a byproduct of the synthesis and degradation of diverse biomolecules. The accumulation of PPi in the cell can result in cell death. Although the substrate is the same, there are variations in the catalysis and features of these enzymes. Two enzyme forms have been identified in bacteria: cytoplasmic or soluble pyrophosphatases and membrane-bound pyrophosphatases, which play major roles in cell bioenergetics. In eukaryotic cells, cytoplasmic enzymes are the predominant form of PPases (c-PPases), while membrane enzymes (m-PPases) are found only in protists and plants. The study of bacterial cytoplasmic and membrane-bound pyrophosphatases has slowed in recent years. These enzymes are central to cell metabolism and physiology since phospholipid and nucleic acid synthesis release important amounts of PPi that must be removed to allow biosynthesis to continue. In this review, two aims were pursued: first, to provide insight into the structural features of PPases known to date and that are well characterized, and to provide examples of enzymes with novel features. Second, the scientific community should continue studying these enzymes because they have many biotechnological applications. Additionally, in this review, we provide evidence that there are m-PPases present in fungi; to date, no examples have been characterized. Therefore, the diversity of PPase enzymes is still a fruitful field of research. Additionally, we focused on the roles of H+/Na+ pumps and m-PPases in cell bioenergetics. Finally, we provide some examples of the applications of these enzymes in molecular biology and biotechnology, especially in plants. This review is valuable for professionals in the biochemistry field of protein structure–function relationships and experts in other fields, such as chemistry, nanotechnology, and plant sciences.

De-nitrosylation Coordinates Appressorium Function for Infection of the Rice Blast Fungus.

As a signaling molecule, nitric oxide (NO) regulates the development and stress response in different organisms. The major biological activity of NO is protein S-nitrosylation, whose function in fungi remains largely unclear. Here, it is found in the rice blast fungus Magnaporthe oryzae, de-nitrosylation process is essential for functional appressorium formation during infection. Nitrosative stress caused by excessive accumulation of NO is harmful for fungal infection. While the S-nitrosoglutathione reductase GSNOR-mediated de-nitrosylation removes excess NO toxicity during appressorium formation to promote infection. Through an indoTMT switch labeling proteomics technique, 741 S-nitrosylation sites in 483 proteins are identified. Key appressorial proteins, such as Mgb1, MagB, Sps1, Cdc42, and septins, are activated by GSNOR through de-nitrosylation. Removing S-nitrosylation sites of above proteins is essential for proper protein structure and appressorial function. Therefore, GSNOR-mediated de-nitrosylation is an essential regulator for appressorium formation. It is also shown that breaking NO homeostasis by NO donors, NO scavengers, as well as chemical inhibitor of GSNOR, shall be effective methods for fungal disease control.

Insights into taxadiene synthase catalysis and promiscuity facilitated by mutability landscape and molecular dynamics

Main conclusionProtein modeling, carbocation docking, and molecular dynamics along with structure-based mutability landscapes provided insight into taxadiene synthase catalysis (first step of the anticancer Taxol biosynthesis), protein structure–function correlations, and promiscuity.Plant terpenes belong to one of the largest and most diverse classes of natural products. This diversity is driven by the terpene synthase enzyme family which comprises numerous different synthases, several of which are promiscuous. Taxadiene synthase (TXS) is a class I diterpene synthase that catalyzes the first step in the biosynthesis pathway of the diterpene Taxol, an anticancer natural product produced by the Taxus plant. Exploring the molecular basis of TXS catalysis and its promiscuous potential garnered interest as a necessary means for understanding enzyme evolution and engineering possibilities to improve Taxol biosynthesis. A catalytically active closed conformation TXS model was designed using the artificial intelligence system, AlphaFold, accompanied by docking and molecular dynamics simulations. In addition, a mutability landscape of TXS including 14 residues was created to probe for structure–function relations. The mutability landscape revealed no mutants with improved catalytic activity compared to wild-type TXS. However, mutations of residues V584, Q609, V610, and Y688 showed high degree of promiscuity producing cembranoid-type and/or verticillene-type major products instead of taxanes. Mechanistic insights into V610F, V584M, Q609A, and Y688C mutants compared to the wild type revealed the trigger(s) for product profile change. Several mutants spanning residues V584, Q609, Y688, Y762, Q770, and F834 increased production of taxa-4(20),11(12)-diene which is a more favorable substrate for Taxol production compared to taxa-4(5),11(12)-diene. Finally, molecular dynamics simulations of the TXS reaction cascade revealed residues involved in ionization, carbocation stabilization, and cyclization ushering deeper understanding of the enzyme catalysis mechanism.Graphical abstract

Water slowing down drives the occurrence of the low temperature dynamical transition in microgels.

The protein dynamical transition marks an increase in atomic mobility and the onset of anharmonic motions at a critical temperature (T d), which is considered relevant for protein functionality. This phenomenon is ubiquitous, regardless of protein composition, structure and biological function and typically occurs at large protein content, to avoid water crystallization. Recently, a dynamical transition has also been reported in non-biological macromolecules, such as poly(N-isopropyl acrylamide) (PNIPAM) microgels, bearing many similarities to proteins. While the generality of this phenomenon is well-established, the role of water in the transition remains a subject of debate. In this study, we use atomistic molecular dynamics (MD) simulations and elastic incoherent neutron scattering (EINS) experiments with selective deuteration to investigate the microscopic origin of the dynamical transition and distinguish water and PNIPAM roles. While a standard analysis of EINS experiments would suggest that the dynamical transition occurs in PNIPAM and water at a similar temperature, simulations reveal a different perspective, also qualitatively supported by experiments. From room temperature down to about 180 K, PNIPAM exhibits only modest changes of dynamics, while water, being mainly hydration water under the probed extreme confinement, significantly slows down and undergoes a mode-coupling transition from diffusive to activated. Our findings therefore challenge the traditional view of the dynamical transition, demonstrating that it occurs in proximity of the water mode-coupling transition, shedding light on the intricate interplay between polymer and water dynamics.

Impact of transition to a hybrid model of biochemistry course-based undergraduate research experience during the COVID-19 pandemic on student science self-efficacy and conceptual knowledge

The COVID-19 outbreak has had a significant impact on higher education worldwide. In-person courses had to be quickly transited to online, including lab courses embedded with Course-based Undergraduate Research Experiences (CUREs). In response to this challenge, we successfully converted a fully in-person biochemistry lab that integrated with a 6-week modular CURE (mCURE) into a hybrid CURE (hCURE) in Fall 2020, with support from the Malate dehydrogenase CUREs Community. The hCURE was structured to have in-person labs and online activities arranged on an alternating weekly basis, so that only half of the regular class size of students attended the hands-on labs at any given time to maintain proper social distancing. To evaluate the efficacy of the hCURE, student science self-efficacy and conceptual understanding of protein structure–function relationships were measured using pre-course and post-course surveys and tests, respectively. Our data showed a significant increase in student science self-efficacy and conceptual knowledge test scores. Furthermore, we compared the pre-lab quiz scores that assessed various biochemical concepts and skills across three different semesters, Fall 2019 with a fully in-person mCURE before the pandemic, Fall 2020 with the hCURE implemented during the pandemic, and Fall 2021 when the lab returned to the fully in-person mCURE following the pandemic. A significant decline in quiz scores from Fall 2019 to Fall 2020, and an even further decline from Fall 2019 to Fall 2021 were observed, suggesting that apart from the impact of course modality, the pandemic may have exerted a lasting adverse effect on student learning.

Sub-THz acoustic excitation of protein motion.

The application of terahertz radiation has been shown to affect both protein structure and cellular function. As the key to such structural changes lies in the dynamic response of a protein, we focus on the susceptibility of the protein's internal dynamics to mechanical stress induced by acoustic pressure waves. We use the open-boundary molecular dynamics method, which allows us to simulate the protein exposed to acoustic waves. By analyzing the dynamic fluctuations of the protein subunits, we demonstrate that the protein is highly susceptible to acoustic waves with frequencies corresponding to those of the internal protein vibrations. This is confirmed by changes in the compactness of the protein structure. As the amplitude of the pressure wave increases, even larger deviations from average positions and larger changes in protein compactness are observed. Furthermore, performing the mode-projection analysis, we show that the breathing-like character of collective modes is enhanced at frequencies corresponding to those used to excite the protein.

Relationship between histone demethylase LSD family and development and prognosis of gastric cancer.

to elucidate the correlation between histone demethylase and gastric cancer. histone demethylase and gastric cancer. As one of the important regulatory mechanisms in molecular biology and epigenetics, histone modification plays an important role in gastric cancer including downstream gene expression regulation and epigenetics effect. Both histone methyltransferase and histone demethylases are involved in the formation and maintaining different of histone methylation status, which in turn through a variety of vital molecules and signaling pathways involved in the recognition of histone methylation modification caused by the downstream biological process, eventually participate in the regulation of chromatin function, and with a variety of important physiological activities, especially closely related to the occurrence of gastric cancer and embryonic development. This paper intends to review the research progress in this field from the aspects of histone methylation modification and the protein structure, catalytic mechanism and biological function of the important histone demethylases LSD1 and LSD2, in order to provide the theoretical reference for further understanding and exploration of histone demethylases in development and prognosis of gastric cancer.

Bioinformatic Approaches for Characterizing Molecular Structure and Function of Food Proteins.

Structural bioinformatics analyzes protein structural models with the goal of uncovering molecular drivers of food functionality. This field aims to develop tools that can rapidly extract relevant information from protein databases as well as organize this information for researchers interested in studying protein functionality. Food bioinformaticians take advantage of millions of protein amino acid sequences and structures contained within these databases, extracting features such as surface hydrophobicity that are then used to model functionality, including solubility, thermostability, and emulsification. This work is aided by a protein structure-function relationship framework, in which bioinformatic properties are linked to physicochemical experimentation. Strong bioinformatic correlations exist for protein secondary structure, electrostatic potential, and surface hydrophobicity. Modeling changes in protein structures through molecular mechanics is an increasingly accessible field that will continue to propel food science research.

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Experimental DermatologyVolume 31, Issue 12 p. i-i COVER IMAGEFree Access Cover Image First published: 01 December 2022 https://doi.org/10.1111/exd.14714AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Graphical Abstract Cover Illustration: The cover image is based on the Concise Communication The effects of missense OPN3 mutations in melanocytic lesions on protein structure and light-sensitive function by Wei Zhang et al., https://doi.org/10.1111/exd.14666. Volume31, Issue12December 2022Pages i-i RelatedInformation

Effects of External Perturbations on Protein Systems: A Microscopic View.

Protein folding can be viewed as the origami engineering of biology resulting from the long process of evolution. Even decades after its recognition, research efforts worldwide focus on demystifying molecular factors that underlie protein structure-function relationships; this is particularly relevant in the era of proteopathic disease. A complex co-occurrence of different physicochemical factors such as temperature, pressure, solvent, cosolvent, macromolecular crowding, confinement, and mutations that represent realistic biological environments are known to modulate the folding process and protein stability in unique ways. In the current review, we have contextually summarized the substantial efforts in unveiling individual effects of these perturbative factors, with major attention toward bottom-up approaches. Moreover, we briefly present some of the biotechnological applications of the insights derived from these studies over various applications including pharmaceuticals, biofuels, cryopreservation, and novel materials. Finally, we conclude by summarizing the challenges in studying the combined effects of multifactorial perturbations in protein folding and refer to complementary advances in experiment and computational techniques that lend insights to the emergent challenges.

Intrinsic protein disorder and conditional folding in AlphaFoldDB.

Intrinsically disordered regions (IDRs) defying the traditional protein structure–function paradigm have been difficult to analyze. The availability of accurate structure predictions on a large scale in AlphaFoldDB offers a fresh perspective on IDR prediction. Here, we establish three baselines for IDR prediction from AlphaFoldDB models based on the recent CAID dataset. Surprisingly, AlphaFoldDB is highly competitive for predicting both IDRs and conditionally folded binding regions, demonstrating the plasticity of the disorder to structure continuum.

Structural and biochemical insights into the association between ERAP1 polymorphism and autoimmune diseases

Autoimmune diseases afflict nearly 10% of the world's population and have a serious impact on survival and quality of life. Unfortunately, the specific pathogenesis of almost all autoimmune diseases is still unclear, with more research findings identifying some key pathogenic genes at the genetic level and several pathogenic inflammatory factor phenotypes. ERAP1 has been suggested as a potential therapeutic target for several autoimmune diseases, especially MHC-Ⅰ related. How the structure and antigenic peptide processing function of ERAP1 affect the pathogenesis of these autoimmune diseases needs to be elucidated more clearly. Genetic studies on single nucleotide polymorphism of ERAP1 provide a good bridge to better understand the relationship and pattern between ERAP1 structure, function, and disease. However, existing reviews have focused on the genetic association of ERAP1 SNPs with autoimmune diseases, and no one has specifically addressed how ERAP1 gene polymorphisms embodied at the protein level specifically mediate antigenic peptide editing and the development of multiple autoimmune diseases. In this paper, we present a comprehensive review of these ERAP1 SNPs associated with multiple autoimmune diseases, in particular the polymorphisms affecting their protein structure and enzyme function, and attempt to unravel the underlying structural and biochemical mechanisms by which ERAP1 affects the pathogenesis of multiple autoimmune diseases through the SNP-protein structure-function-disease relationship. This study will provide theoretical help and ideas for understanding the relationship between ERAP1 and autoimmune diseases and for drug design targeting wild-type and mutant proteins with different polymorphisms.

Intrinsically disordered protein, DNA binding with one finger transcription factor (OsDOF27) implicates thermotolerance in yeast and rice.

Intrinsically disorder regions or proteins (IDRs or IDPs) constitute a large subset of the eukaryotic proteome, which challenges the protein structure–function paradigm. These IDPs lack a stable tertiary structure, yet they play a crucial role in the diverse biological process of plants. This study represents the intrinsically disordered nature of a plant-specific DNA binding with one finger transcription factor (DOF-TF). Here, we have investigated the role of OsDOF27 and characterized it as an intrinsically disordered protein. Furthermore, the molecular role of OsDOF27 in thermal stress tolerance has been elucidated. The qRT-PCR analysis revealed that OsDOF27 was significantly upregulated under different abiotic stress treatments in rice, particularly under heat stress. The stress-responsive transcript induction of OsDOF27 was further correlated with enriched abiotic stress-related cis-regulatory elements present in its promoter region. The in vivo functional analysis of the potential role of OsDOF27 in thermotolerance was further studied in yeast and in planta. Ectopic expression of OsDOF27 in yeast implicates thermotolerance response. Furthermore, the rice transgenic lines with overexpressing OsDOF27 revealed a positive role in mitigating heat stress tolerance. Collectively, our results evidently show the intrinsically disorderedness in OsDOF27 and its role in thermal stress response in rice.

Insights into Membrane Curvature Sensing and Membrane Remodeling by Intrinsically Disordered Proteins and Protein Regions.

Cellular membranes are highly dynamic in shape. They can rapidly and precisely regulate their shape to perform various cellular functions. The protein’s ability to sense membrane curvature is essential in various biological events such as cell signaling and membrane trafficking. As they are bound, these curvature-sensing proteins may also change the local membrane shape by one or more curvature driving mechanisms. Established curvature-sensing/driving mechanisms rely on proteins with specific structural features such as amphipathic helices and intrinsically curved shapes. However, the recent discovery and characterization of many proteins have shattered the protein structure–function paradigm, believing that the protein functions require a unique structural feature. Typically, such structure-independent functions are carried either entirely by intrinsically disordered proteins or hybrid proteins containing disordered regions and structured domains. It is becoming more apparent that disordered proteins and regions can be potent sensors/inducers of membrane curvatures. In this article, we outline the basic features of disordered proteins and regions, the motifs in such proteins that encode the function, membrane remodeling by disordered proteins and regions, and assays that may be employed to investigate curvature sensing and generation by ordered/disordered proteins.Graphical

LOMETS3: integrating deep learning and profile alignment for advanced protein template recognition and function annotation.

Deep learning techniques have significantly advanced the field of protein structure prediction. LOMETS3 (https://zhanglab.ccmb.med.umich.edu/LOMETS/) is a new generation meta-server approach to template-based protein structure prediction and function annotation, which integrates newly developed deep learning threading methods. For the first time, we have extended LOMETS3 to handle multi-domain proteins and to construct full-length models with gradient-based optimizations. Starting from a FASTA-formatted sequence, LOMETS3 performs four steps of domain boundary prediction, domain-level template identification, full-length template/model assembly and structure-based function prediction. The output of LOMETS3 contains (i) top-ranked templates from LOMETS3 and its component threading programs, (ii) up to 5 full-length structure models constructed by L-BFGS (limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm) optimization, (iii) the 10 closest Protein Data Bank (PDB) structures to the target, (iv) structure-based functional predictions, (v) domain partition and assembly results, and (vi) the domain-level threading results, including items (i)–(iii) for each identified domain. LOMETS3 was tested in large-scale benchmarks and the blind CASP14 (14th Critical Assessment of Structure Prediction) experiment, where the overall template recognition and function prediction accuracy is significantly beyond its predecessors and other state-of-the-art threading approaches, especially for hard targets without homologous templates in the PDB. Based on the improved developments, LOMETS3 should help significantly advance the capability of broader biomedical community for template-based protein structure and function modelling.

1H, 13C and 15N resonance assignments of TFIIS LW domain from Saccharomyces cerevisiae.

TFIIS is one of the best-characterized transcription elongation factors, with a domain I (named also as LW domain) in the N-terminus. It can relieve the arrest of RNA polymerase II (RNAP II) when the elongation of RNAP II is impaired. Here we report the resonance assignments of the protein backbone and side chains of the LW domain of TFIIS from S. cerevisiae, the secondary structure prediction indicates the ScTFIIS LW domain contains six α-helices with no β-sheet, which will lay the foundation for the protein structure determination and function elucidation.

High-dimensionality reduction clustering of complex carbohydrates to study lung cancer metabolic heterogeneity.

The tumor microenvironment contains a heterogeneous population of stromal and cancer cells that engage in metabolic crosstalk to ultimately promote tumor growth and contribute to progression. Due to heterogeneity within solid tumors, pooled mass spectrometry workflows are less sensitive at delineating unique metabolic perturbations between stromal and immune cell populations. Two critical, but understudied, facets of glucose metabolism are anabolic pathways for glycogen and N-linked glycan biosynthesis. Together, these complex carbohydrates modulate bioenergetics and protein-structure function, and create functional microanatomy in distinct cell populations within the tumor heterogeneity. Herein, we combine high-dimensionality reduction and clustering (HDRC) analysis with matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) and demonstrate its ability for the comprehensive assessment of tissue histopathology and metabolic heterogeneity in human FFPE sections. In human lung adenocarcinoma (LUAD) tumor tissues, HDRC accurately clusters distinct regions and cell populations within the tumor microenvironment, including tumor cells, tumor-infiltrating lymphocytes, cancer-associated fibroblasts, and necrotic regions. In-depth pathway enrichment analyses revealed unique metabolic pathways are associated with each distinct pathological region. Further, we highlight the potential of HDRC analysis to study complex carbohydrate metabolism in a case study of lung cancer disparity. Collectively, our results demonstrate the promising potentials of HDRC of pixel-based carbohydrate analysis to study cell-type and regional-specific stromal signaling within the tumor microenvironment.

Protein structural databases in computational biology

Protein databases have becomes a Pivotal part of computational biology. Huge amount of data for protein structure function and particularly sequence are being generated. Searching database is a first step of study to find new protein. We introduce the basics of protein structural bioinformatics. Protein perform most essential biological and chemical function in a cell. They also play important role in structural, enzymatic, transport and regulatory functions. and is determined by their structure. This review covers some basic of protein structure and associated databases and it is more advanced topic of protein structural bioinformatics. This work provides to explore the potential of protein databases on internet.